Inscriere cercetatori

Premii Ad Astra

premii Ad Astra

Asociația Ad Astra a anunțat câștigătorii Premiilor Ad Astra 2022: http://premii.ad-astra.ro/. Proiectul și-a propus identificarea și popularizarea modelelor de succes, a rezultatelor excepționale ale cercetătorilor români din țară și din afara ei.

Asociatia Ad Astra a cercetatorilor romani lanseaza BAZA DE DATE A CERCETATORILOR ROMANI DIN DIASPORA. Scopul acestei baze de date este aceea de a stimula colaborarea dintre cercetatorii romani de peste hotare dar si cu cercetatorii din Romania. Cercetatorii care doresc sa fie nominalizati in aceasta baza de date sunt rugati sa trimita un email la cristian.presura@gmail.com

Automated parameterisation for multi-scale image segmentation on multiple layers

Domenii publicaţii > Ştiinţele pământului şi planetare + Tipuri publicaţii > Articol în revistã ştiinţificã

Autori: Lucian Drãguţ, Ovidiu Csillik, Clemens Eisank, Dirk Tiede

Editorial: ISPRS Journal of Photogrammetry and Remote Sensing, 88, p.119-127, 2014.

Rezumat:

We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognitionŸ software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis.

Cuvinte cheie: Automation; Imagery; Object; Representation; GEOBIA; MRS

URL: http://www.sciencedirect.com/science/article/pii/S0924271613002803